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A compounding LLM-maintained knowledge wiki.

Synthesis of Andrej Karpathy's persistent-wiki gist and milla-jovovich's
mempalace, with an automation layer on top for conversation mining, URL
harvesting, human-in-the-loop staging, staleness decay, and hygiene.

Includes:
- 11 pipeline scripts (extract, summarize, index, harvest, stage,
  hygiene, maintain, sync, + shared library)
- Full docs: README, SETUP, ARCHITECTURE, DESIGN-RATIONALE, CUSTOMIZE
- Example CLAUDE.md files (wiki schema + global instructions) tuned for
  the three-collection qmd setup
- 171-test pytest suite (cross-platform, runs in ~1.3s)
- MIT licensed
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# LLM Wiki — Compounding Knowledge for AI Agents
A persistent, LLM-maintained knowledge base that sits between you and the
sources it was compiled from. Unlike RAG — which re-discovers the same
answers on every query — the wiki **gets richer over time**. Facts get
cross-referenced, contradictions get flagged, stale advice ages out and
gets archived, and new knowledge discovered during a session gets written
back so it's there next time.
The agent reads the wiki at the start of every session and updates it as
new things are learned. The wiki is the long-term memory; the session is
the working memory.
> **Inspiration**: this combines the ideas from
> [Andrej Karpathy's persistent-wiki gist](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f)
> and [milla-jovovich/mempalace](https://github.com/milla-jovovich/mempalace),
> and adds an automation layer on top so the wiki maintains itself.
---
## The problem with stateless RAG
Most people's experience with LLMs and documents looks like RAG: you upload
files, the LLM retrieves chunks at query time, generates an answer, done.
This works — but the LLM is rediscovering knowledge from scratch on every
question. There's no accumulation.
Ask the same subtle question twice and the LLM does all the same work twice.
Ask something that requires synthesizing five documents and the LLM has to
find and piece together the relevant fragments every time. Nothing is built
up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
Worse, raw sources go stale. URLs rot. Documentation lags. Blog posts
get retracted. If your knowledge base is "the original documents,"
stale advice keeps showing up alongside current advice and there's no way
to know which is which.
## The core idea — a compounding wiki
Instead of retrieving from raw documents at query time, the LLM
**incrementally builds and maintains a persistent wiki** — a structured,
interlinked collection of markdown files that sits between you and the
raw sources.
When a new source shows up (a doc page, a blog post, a CLI `--help`, a
conversation transcript), the LLM doesn't just index it. It reads it,
extracts what's load-bearing, and integrates it into the existing wiki —
updating topic pages, revising summaries, noting where new data
contradicts old claims, strengthening or challenging the evolving
synthesis. The knowledge is compiled once and then *kept current*, not
re-derived on every query.
This is the key difference: **the wiki is a persistent, compounding
artifact.** The cross-references are already there. The contradictions have
already been flagged. The synthesis already reflects everything the LLM
has read. The wiki gets richer with every source added and every question
asked.
You never (or rarely) write the wiki yourself. The LLM writes and maintains
all of it. You're in charge of sourcing, exploration, and asking the right
questions. The LLM does the summarizing, cross-referencing, filing, and
bookkeeping that make a knowledge base actually useful over time.
---
## What this adds beyond Karpathy's gist
Karpathy's gist describes the *idea* — a wiki the agent maintains. This
repo is a working implementation with an automation layer that handles the
lifecycle of knowledge, not just its creation:
| Layer | What it does |
|-------|--------------|
| **Conversation mining** | Extracts Claude Code session transcripts into searchable markdown. Summarizes them via `claude -p` with model routing (haiku for short sessions, sonnet for long ones). Links summaries to wiki pages by topic. |
| **URL harvesting** | Scans summarized conversations for external reference URLs. Fetches them via `trafilatura` → `crawl4ai` → stealth mode cascade. Compiles clean markdown into pending wiki pages. |
| **Human-in-the-loop staging** | Automated content lands in `staging/` with `status: pending`. You review via CLI, interactive prompts, or an in-session Claude review. Nothing automated goes live without approval. |
| **Staleness decay** | Every page tracks `last_verified`. After 6 months without a refresh signal, confidence decays `high → medium`; 9 months → `low`; 12 months → `stale` → auto-archived. |
| **Auto-restoration** | Archived pages that get referenced again in new conversations or wiki updates are automatically restored. |
| **Hygiene** | Daily structural checks (orphans, broken cross-refs, index drift, frontmatter repair). Weekly LLM-powered checks (duplicates, contradictions, missing cross-references). |
| **Orchestrator** | One script chains all of the above into a daily cron-able pipeline. |
The result: you don't have to maintain the wiki. You just *use* it. The
automation handles harvesting new knowledge, retiring old knowledge,
keeping cross-references intact, and flagging ambiguity for review.
---
## Why each part exists
Before implementing anything, the design was worked out interactively
with Claude as a [Signal & Noise analysis of Karpathy's
pattern](https://claude.ai/public/artifacts/0f6e1d9b-3b8c-43df-99d7-3a4328a1620c).
That analysis found seven real weaknesses in the core pattern. This
repo exists because each weakness has a concrete mitigation — and
every component maps directly to one:
| Karpathy-pattern weakness | How this repo answers it |
|---------------------------|--------------------------|
| **Errors persist and compound** | `confidence` field with time-based decay → pages age out visibly. Staging review catches automated content before it goes live. Full-mode hygiene does LLM contradiction detection. |
| **Hard ~50K-token ceiling** | `qmd` (BM25 + vector + re-ranking) set up from day one. Wing/room structural filtering narrows search before retrieval. Archive collection is excluded from default search. |
| **Manual cross-checking returns** | Every wiki claim traces back to immutable `raw/harvested/*.md` with SHA-256 hash. Staging review IS the cross-check. `compilation_notes` field makes review fast. |
| **Knowledge staleness** (the #1 failure mode in community data) | Daily + weekly cron removes "I forgot" as a failure mode. `last_verified` auto-refreshes from conversation references. Decayed pages auto-archive. |
| **Cognitive outsourcing risk** | Staging review forces engagement with every automated page. `qmd query` makes retrieval an active exploration. Wake-up briefing ~200 tokens the human reads too. |
| **Weaker semantic retrieval** | `qmd` hybrid (BM25 + vector). Full-mode hygiene adds missing cross-references. Structural metadata (wings, rooms) complements semantic search. |
| **No access control** | Git sync with `merge=union` markdown handling. Network-boundary ACL via Tailscale is the suggested path. *This one is a residual trade-off — see [DESIGN-RATIONALE.md](docs/DESIGN-RATIONALE.md).* |
The short version: Karpathy published the idea, the community found the
holes, and this repo is the automation layer that plugs the holes.
See **[`docs/DESIGN-RATIONALE.md`](docs/DESIGN-RATIONALE.md)** for the
full argument with honest residual trade-offs and what this repo
explicitly does NOT solve.
---
## Compounding loop
```
┌─────────────────────┐
│ Claude Code │
│ sessions (.jsonl) │
└──────────┬──────────┘
│ extract-sessions.py (hourly, no LLM)
▼
┌─────────────────────┐
│ conversations/ │ markdown transcripts
│ <project>/*.md │ (status: extracted)
└──────────┬──────────┘
│ summarize-conversations.py --claude (daily)
▼
┌─────────────────────┐
│ conversations/ │ summaries with related: wiki links
│ <project>/*.md │ (status: summarized)
└──────────┬──────────┘
│ wiki-harvest.py (daily)
▼
┌─────────────────────┐
│ raw/harvested/ │ fetched URL content
│ *.md │ (immutable source material)
└──────────┬──────────┘
│ claude -p compile step
▼
┌─────────────────────┐
│ staging/<type>/ │ pending pages
│ *.md │ (status: pending, origin: automated)
└──────────┬──────────┘
│ human review (wiki-staging.py --review)
▼
┌─────────────────────┐
│ patterns/ │ LIVE wiki
│ decisions/ │ (origin: manual or promoted-from-automated)
│ concepts/ │
│ environments/ │
└──────────┬──────────┘
│ wiki-hygiene.py (daily quick / weekly full)
│ - refresh last_verified from new conversations
│ - decay confidence on idle pages
│ - auto-restore archived pages referenced again
│ - fuzzy-fix broken cross-references
▼
┌─────────────────────┐
│ archive/<type>/ │ stale/superseded content
│ *.md │ (excluded from default search)
└─────────────────────┘
```
Every arrow is automated. The only human step is staging review — and
that's quick because the AI compilation step already wrote the page, you
just approve or reject.
---
## Quick start — two paths
### Path A: just the idea (Karpathy-style)
Open a Claude Code session in an empty directory and tell it:
```
I want you to start maintaining a persistent knowledge wiki for me.
Create a directory structure with patterns/, decisions/, concepts/, and
environments/ subdirectories. Each page should have YAML frontmatter with
title, type, confidence, sources, related, last_compiled, and last_verified
fields. Create an index.md at the root that catalogs every page.
From now on, when I share a source (a doc page, a CLI --help, a conversation
I had), read it, extract what's load-bearing, and integrate it into the
wiki. Update existing pages when new knowledge refines them. Flag
contradictions between pages. Create new pages when topics aren't
covered yet. Update index.md every time you create or remove a page.
When I ask a question, read the relevant wiki pages first, then answer.
If you rely on a wiki page with `confidence: low`, flag that to me.
```
That's the whole idea. The agent will build you a growing markdown tree
that compounds over time. This is the minimum viable version.
### Path B: the full automation (this repo)
```bash
git clone <this-repo> ~/projects/wiki
cd ~/projects/wiki
# Install the Python extraction tools
pipx install trafilatura
pipx install crawl4ai && crawl4ai-setup
# Install qmd for full-text + vector search
npm install -g @tobilu/qmd
# Configure qmd (3 collections — see docs/SETUP.md for the YAML)
# Edit scripts/extract-sessions.py with your project codes
# Edit scripts/update-conversation-index.py with matching display names
# Copy the example CLAUDE.md files (wiki schema + global instructions)
cp docs/examples/wiki-CLAUDE.md CLAUDE.md
cat docs/examples/global-CLAUDE.md >> ~/.claude/CLAUDE.md
# edit both for your conventions
# Run the full pipeline once, manually
bash scripts/mine-conversations.sh --extract-only # Fast, no LLM
python3 scripts/summarize-conversations.py --claude # Classify + summarize
python3 scripts/update-conversation-index.py --reindex
# Then maintain
bash scripts/wiki-maintain.sh # Daily hygiene
bash scripts/wiki-maintain.sh --hygiene-only --full # Weekly deep pass
```
See [`docs/SETUP.md`](docs/SETUP.md) for complete setup including qmd
configuration (three collections: `wiki`, `wiki-archive`,
`wiki-conversations`), optional cron schedules, git sync, and the
post-merge hook. See [`docs/examples/`](docs/examples/) for starter
`CLAUDE.md` files (wiki schema + global instructions) with explicit
guidance on using the three qmd collections.
---
## Directory layout after setup
```
wiki/
├── CLAUDE.md ← Schema + instructions the agent reads every session
├── index.md ← Content catalog (the agent reads this first)
├── patterns/ ← HOW things should be built (LIVE)
├── decisions/ ← WHY we chose this approach (LIVE)
├── concepts/ ← WHAT the foundational ideas are (LIVE)
├── environments/ ← WHERE implementations differ (LIVE)
├── staging/ ← PENDING automated content awaiting review
│ ├── index.md
│ └── <type>/
├── archive/ ← STALE / superseded (excluded from search)
│ ├── index.md
│ └── <type>/
├── raw/ ← Immutable source material (never modified)
│ ├── <topic>/
│ └── harvested/ ← URL harvester output
├── conversations/ ← Mined Claude Code session transcripts
│ ├── index.md
│ └── <project>/
├── context/ ← Auto-updated AI session briefing
│ ├── wake-up.md ← Loaded at the start of every session
│ └── active-concerns.md ← Current blockers and focus areas
├── reports/ ← Hygiene operation logs
├── scripts/ ← The automation pipeline
├── tests/ ← Pytest suite (171 tests)
├── .harvest-state.json ← URL dedup state (committed, synced)
├── .hygiene-state.json ← Content hashes, deferred issues (committed, synced)
└── .mine-state.json ← Conversation extraction offsets (gitignored, per-machine)
```
---
## What's Claude-specific (and what isn't)
This repo is built around **Claude Code** as the agent. Specifically:
1. **Session mining** expects `~/.claude/projects/<hashed-path>/*.jsonl`
files written by the Claude Code CLI. Other agents won't produce these.
2. **Summarization** uses `claude -p` (the Claude Code CLI's one-shot mode)
with haiku/sonnet routing by conversation length. Other LLM CLIs would
need a different wrapper.
3. **URL compilation** uses `claude -p` to turn raw harvested content into
a wiki page with proper frontmatter.
4. **The agent itself** (the thing that reads `CLAUDE.md` and maintains the
wiki conversationally) is Claude Code. Any agent that reads markdown
and can write files could do this job — `CLAUDE.md` is just a text
file telling the agent what the wiki's conventions are.
**What's NOT Claude-specific**:
- The wiki schema (frontmatter, directory layout, lifecycle states)
- The staleness decay model and archive/restore semantics
- The human-in-the-loop staging workflow
- The hygiene checks (orphans, broken cross-refs, duplicates)
- The `trafilatura` + `crawl4ai` URL fetching
- The qmd search integration
- The git-based cross-machine sync
If you use a different agent, you replace parts **1-4** above with
equivalents for your agent. The other 80% of the repo is agent-agnostic.
See [`docs/CUSTOMIZE.md`](docs/CUSTOMIZE.md) for concrete adaptation
recipes.
---
## Architecture at a glance
Eleven scripts organized in three layers:
**Mining layer** (ingests conversations):
- `extract-sessions.py` — Parse Claude Code JSONL → markdown transcripts
- `summarize-conversations.py` — Classify + summarize via `claude -p`
- `update-conversation-index.py` — Regenerate conversation index + wake-up context
**Automation layer** (maintains the wiki):
- `wiki_lib.py` — Shared frontmatter parser, `WikiPage` dataclass, constants
- `wiki-harvest.py` — URL classification + fetch cascade + compile to staging
- `wiki-staging.py` — Human review (list/promote/reject/review/sync)
- `wiki-hygiene.py` — Quick + full hygiene checks, archival, auto-restore
- `wiki-maintain.sh` — Top-level orchestrator chaining harvest + hygiene
**Sync layer**:
- `wiki-sync.sh` — Git commit/pull/push with merge-union markdown handling
- `mine-conversations.sh` — Mining orchestrator
See [`docs/ARCHITECTURE.md`](docs/ARCHITECTURE.md) for a deeper tour.
---
## Why markdown, not a real database?
Markdown files are:
- **Human-readable without any tooling** — you can browse in Obsidian, VS Code, or `cat`
- **Git-native** — full history, branching, rollback, cross-machine sync for free
- **Agent-friendly** — every LLM was trained on markdown, so reading and writing it is free
- **Durable** — no schema migrations, no database corruption, no vendor lock-in
- **Interoperable** — Obsidian graph view, `grep`, `qmd`, `ripgrep`, any editor
A SQLite file with the same content would be faster to query but harder
to browse, harder to merge, harder to audit, and fundamentally less
*collaborative* between you and the agent. Markdown wins for knowledge
management what Postgres wins for transactions.
---
## Testing
Full pytest suite in `tests/` — 171 tests across all scripts, runs in
**~1.3 seconds**, no network or LLM calls needed, works on macOS and
Linux/WSL.
```bash
cd tests && python3 -m pytest
# or
bash tests/run.sh
```
The test suite uses a disposable `tmp_wiki` fixture so no test ever
touches your real wiki.
---
## Credits and inspiration
This repo is a synthesis of two existing ideas with an automation layer
on top. It would not exist without either of them.
**Core pattern — [Andrej Karpathy — "Agent-Maintained Persistent Wiki" gist](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f)**
The foundational idea of a compounding LLM-maintained wiki that moves
synthesis from query-time (RAG) to ingest-time. This repo is an
implementation of Karpathy's pattern with the community-identified
failure modes plugged.
**Structural memory taxonomy — [milla-jovovich/mempalace](https://github.com/milla-jovovich/mempalace)**
The wing/room/hall/closet/drawer/tunnel concepts that turn a flat
corpus into something you can navigate without reading everything. See
[`ARCHITECTURE.md#borrowed-concepts`](docs/ARCHITECTURE.md#borrowed-concepts)
for the explicit mapping of MemPalace terms to this repo's
implementation.
**Search layer — [qmd](https://github.com/tobi/qmd)** by Tobi Lütke
(Shopify CEO). Local BM25 + vector + LLM re-ranking on markdown files.
Chosen over ChromaDB because it uses the same storage format as the
wiki — one index to maintain, not two. Explicitly recommended by
Karpathy as well.
**URL extraction stack** — [trafilatura](https://github.com/adbar/trafilatura)
for fast static-page extraction and [crawl4ai](https://github.com/unclecode/crawl4ai)
for JS-rendered and anti-bot cases. The two-tool cascade handles
essentially any web content without needing a full browser stack for
simple pages.
**The agent** — [Claude Code](https://claude.com/claude-code) by Anthropic.
The repo is Claude-specific (see the section above for what that means
and how to adapt for other agents).
**Design process** — this repo was designed interactively with Claude
as a structured Signal & Noise analysis before any code was written.
The interactive design artifact is here:
[The LLM Wiki — Karpathy's Pattern — Signal & Noise](https://claude.ai/public/artifacts/0f6e1d9b-3b8c-43df-99d7-3a4328a1620c).
That artifact walks through the seven real strengths and seven real
weaknesses of the core pattern, then works through concrete mitigations
for each weakness. Every component in this repo maps back to a specific
mitigation identified there.
[`docs/DESIGN-RATIONALE.md`](docs/DESIGN-RATIONALE.md) is the condensed
version of that analysis as it applies to this implementation.
---
## License
MIT — see [`LICENSE`](LICENSE).
## Contributing
This is a personal project that I'm making public in case the pattern is
useful to others. Issues and PRs welcome, but I make no promises about
response time. If you fork and make it your own, I'd love to hear how you
adapted it.